Abstract
Gene expression profiles consisting of thousands of genes can describe the characteristics of specific cancer subtype. By efficiently using the overall scheme of gene expression, accurate tumor diagnosis can be performed well in clinical medicine. However, faced many problems such as too much noise and the curse of dimensionality that the number of genes far exceeds the size of samples in tumor dataset, tumor classification by selecting a small set of gene subset from the thousands of genes becomes a challenging task. This paper proposed a novel high accuracy method, which utilized the global scheme of differentially expressed genes corresponding to each tumor subtype which is determined by tumor-related genes, to classify tumor samples by using Minimum Average Correlation Energy (MACE) filter method to computing the similarity degree between a test sample with unknown label in test set and the template constructed with training set. The experimental results obtained on two actual tumor datasets indicate that the proposed method is very effective and robust in classification performance.
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References
Dabney, A.R.: Classification of microarrays to nearest centroids. Bioinformatics 21(22), 4148–4154 (2005)
Wang, L.P., Chu, F., Xie, W.: Accurate cancer classification using expressions of very few genes. IEEE/ACM Transactions on computational biology and bioinformatics 4(1), 40–53 (2007)
Huang, H.L., Lee, C.C., Ho, S.Y.: Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers. BioSystems 90(1), 78–86 (2007)
Sreekumar, J., Jose, K.K.: Statistical tests for identification of differentially expressed genes in cDNA microarray experiments. Indian Journal of Biotechnology 7(4), 423–436 (2008)
Deng, L., Ma, J.W., Pei, J.: Rank sum method for related gene selection and its application to tumor diagnosis. Chinese Science Bulletin 49(15), 1652–1657 (2004)
Li, L.P., Darden, T.A., Weinberg, C.R., Levine, A.J., Pedersen, L.G.: Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Combinatorial Chemistry & High Throughput Screening 4(8), 727–739 (2001)
Zhou, X., Tuck, D.P.: MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics 23(9), 1106–1114 (2007)
Troyanskaya, O.G., Garber, M.E., Brown, P.O., Botstein, D., Altman, R.B.: Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18(11), 1454–1461 (2002)
Lehmann, E.L.: Non-parametrics: Statistical methods based on ranks, Holden-Day, San Francisco (1975)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)
Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association 47(260), 583–621 (1952)
Mahalanobis, A., Kumar, B.V.K., Casasent, D.: Minimum average correlation energy filters. Appl. Opt. 26, 3633–3640 (1987)
Kumar, B.V.: Tutorial survey of composite filter designs for optical correlators. Appl. Opt. 31, 4773–4801 (1992)
Kumar, B.V., Savvides, V.M.K., Xie, C., Thornton, J., Mahalanobis, A.: Biometric verification using advanced correlation filters. Appl. Opt. 43, 391–402 (1992)
Kumar, B.V.: Minimum variance synthetic discriminant functions. Opt. Soc. Am. A 3, 1579–1584 (1986)
Yeoh, E.J., Ross, M.E., Shurtleff, S.A., Williams, W.K., Patel, D., Mahfouz, R., Behm, F.G., Raimondi, S.C., Relling, M.V., Patel, A., Cheng, C., Campana, D., Wilkins, D., Zhou, X., Li, J., Liu, H., Pui, C.H., Evans, W.E., Naeve, C., Wong, L., Downing, J.R.: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1(2), 133–143 (2002)
Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7(6), 673–679 (2001)
Deutsch, J.M.: Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 19(1), 45–52 (2003)
Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America 99(10), 6567–6572 (2002)
Dabney, A.R., Storey, J.D.: Optimality driven nearest centroid classification from genomic data. PLoS ONEÂ 2(10), e1002 (2007), doi:10.1371/journal.pone.0001002
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Wang, S., Zhu, Y. (2009). A Novel Method to Robust Tumor Classification Based on MACE Filter. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_102
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DOI: https://doi.org/10.1007/978-3-642-04020-7_102
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